Zhenhai Gao

Motor imagery (MI) based brain–computer interfaces (BCIs) enable active device control through electroencephalography (EEG), offering contactless human–machine interaction. However, realistic MI-EEG decoding is challenged by non-uniform electrode coupling, long-range rhythmic dynamics, unstable second-order statistics, and blurred boundaries among fine-grained motor intentions. These challenges require topology-aware, statistically stable, and boundary-discriminative EEG representations, which remain insufficiently addressed in realistic decoding scenarios. To address these challenges, we propose ASDL-EEG, a coordinated representation learning framework for robust MI-EEG decoding. First, ASDL-EEG introduces a topology-aware first-order encoder that performs asymmetric electrode-axis spatial aggregation with multi-scale receptive fields and dilated temporal modeling to capture non-uniform spatial dependencies and long-range MI rhythms. Second, the Log-Diagonal Riemannian (LDR) extractor constructs aligned log-diagonal covariance descriptors as a compact, stability-oriented alternative to high-dimensional full covariance representations. To further enhance boundary discrimination, we propose RPA-v2, a sample-specific hard-negative prototype alignment method for fused first- and second-order EEG embeddings enlarging the margin between the target prototype and the most confusing non-target prototype. We further construct CW-MI-5, to the best of our knowledge, the first five-class MI-EEG dataset for intelligent-cockpit car-window control, serving as a naturalistic benchmark with synchronized EEG sensing and cockpit interaction cues. Experiments on CW-MI-5 and BCI Competition IV-2a show superior accuracy and Cohen’s kappa, validating ASDL-EEG in both cockpit interaction and standard MI benchmark settings.

Sideslip angle and velocity are key parameters for assessing vehicle stability during typical cornering and drifting maneuvers, but accurate measurement under varying roads is prohibitively expensive. Therefore, a diffusion-augmented Koopman Kalman filter (Diff-AKKF) is proposed in this article, enabling high-precision estimation of sideslip angle and velocity within and beyond the stability limits across different road surfaces. First, a conditional diffusion model with physical constraint is established to directly predict parameters rather than noise, enabling inference of equivalent dynamics parameters across various maneuvers to provide prior information on dynamics characteristics and state evolution to augment estimation. Subsequently, the Koopman Kalman filter is developed based on Koopman operator theory combined with deep learning, embedding parameters inferred from the diffusion model to estimate sideslip angle and velocity. Finally, real-vehicle experiments involving typical cornering and drifting maneuvers are conducted on both wet and dry asphalt roads, thoroughly validating the effectiveness, estimation accuracy, and robustness of the Diff-AKKF algorithm within and beyond the stability limits scenarios.

Benefiting from its over-actuated characteristics, the distributed vehicle offers enhanced maneuverability and controllability under extreme conditions compared to conventional architectures. Integrated motion control serves as a crucial means to resolve conflicts between actuator functions and achieve dynamic harmony, thus enhancing the accuracy and stability of the trajectory tracking of autonomous vehicles. High-precision motion control relies on the accurate dynamics model. However, the mechanism-based control strategy suffers from structural inaccuracies, environmental disturbances, and parameter perturbations, leading to performance degradation and poor robustness. In this paper, a fusion control strategy is proposed that combines the generalization of the mechanism model with the precision of data-driven learning to predict unmodeled dynamics. Specifically, within the framework of offset free model predictive control (OF-MPC), Bayesian optimization (BO) is introduced to identify the key parameters of the mechanism nominal model under typical coupling motion conditions, and a states-coefficients joint mismatch estimator (SCJME) is trained by physics-informed neural network (PINN) with mechanism constraints introduced in the training process to ensure the stability and convergence of the estimator, which enables the SJCME to predict the unmodeled dynamics and compensate for nominal model mismatch during rolling optimization. In particular, the dual extended Kalman filter (DEKF) is adopted in the construction of SCJME, which adjusts the fusion coefficient online based on the states-observation residuals to mitigate performance degradation in platform migration. In comprehensive scenarios, hardware-in-loop (HIL) tests have validated the effectiveness of the proposed strategy, demonstrating improved precision and stability in coupled motion tracking, along with enhanced robustness during heterogeneous platform migrating.

Motion planning for autonomous driving requires consideration of close interactions between the ego-vehicle and surrounding traffic participants, resulting in a synergistic relationship between path planning and motion prediction tasks. However, existing approaches often rely on static adjacency graphs or decoupled pipelines, making it difficult to capture dynamic interagent dependencies and scene variations. This leads to limited long-horizon predictive capability and planned trajectories that lack collaborative responsiveness and temporal consistency. Especially in dense urban traffic, many multiscale or interaction-aware methods remain prediction-centric and lack a unified mechanism to jointly model multiscale spatiotemporal information, causal constraints, and planning objectives, making it challenging to balance long-term behavioral consistency with short-term risk adaptation. To address this, we propose ACP-MSP, a multiscale unified prediction–planning framework that enhances scene understanding and decision-making through deep integration of prediction and planning. First, we construct a dynamic topology interaction network to unify the representation of multiagent trajectories and environmental elements, and generate multimodal probabilistic trajectories through a multiscale hierarchical structure to improve long-term prediction capability. Second, we design a Transformer-based unified prediction–planning architecture that employs adaptive feature aggregation and cross-task attention to reduce information distortion and enhance collaborative decision-making. Finally, we introduce a causal self-attention mechanism that incorporates temporal masking constraints and scene-dynamic scoring based on obstacle distance and velocity changes, improving the temporal consistency of planned trajectories. Evaluations on the large-scale nuPlan and Val14 benchmarks show that ACP-MSP achieves state-of-the-art performance and demonstrates robust transferability across diverse driving styles.

A game-based cooperative steering control (GCSC) approach is introduced to facilitate effective collaboration between human drivers and automation, incorporating the neuromuscular delay inherent in human responses. In this framework, a dynamic coordination between driver and automation goals is achieved through the establishment of a game equilibrium in instances of driver and automation conflict. In response to the challenges posed by frequent modifications in driving weights and their subsequent burden on human drivers, this article proposes a strategy that integrates fixed initial weights with dynamic adjustments to driver–automation driving weights. Moreover, a comprehensive evaluation method including subjective and objective evaluation indexes is proposed. Different drivers are invited to perform virtual driving experiments, and the experimental results are analyzed by the proposed evaluation method. It is concluded that the driver’s driving weight should be kept at a high level during cooperative steering control when the driver’s intention cannot be perfectly obtained, and the determination of the driving weight should also consider the driver’s driving skills.